Abstract
A Light Field is an imaged based rendering technique based on the eight dimensional Plenoptic Function, simplified to a four dimensional function. It describes the amount of light covering the space, from any point, in any arbitrary direction. It ignores variables like the time and wavelength and assumes that radiance is equal throughout a line in the free space and so it is more efficient than most of the other image based rendering techniques in the literature. Drawbacks appear in the quality of the visualization but may be reduced by the correct use of the technique regarding the desired application. This paper presents a didactic application of this type of imaged based rendering technique that uses multiple sights of a scene through different points of views. The application of light-fields to medical images is thought one of the main objectives of this work and so, a simple application of this image based rendering technique, to this type of images is presented in order to show the usefulness of the approach.

Abstract
We describe a real-time facade tracking system that uses, as setup information, only two images of a facade, captured on the moment. No more previous information is needed, such as a facade 3D model, dimensions or aspect ratio. Feature points and their local descriptors are extracted from that pair of images and used during the detection and tracking of the facade. Additionally, parallax and topological information is also used in order to increase the overall robustness of the tracking process. Experiments show that the system can detect and track a wide variety of facades, including those that are not entirely planar, partially occluded or have few distinguishable visual landmarks. The reliance on on-the-spot information, alone, makes this system useful for Outdoor Augmented Reality applications, in an Anywhere Augmentation urban context.

Abstract
There are several problems in the computer graphics and visualization domains which require optimization tasks to be performed in order to improve the quality of the overall process. In this context, we propose and describe an innovative optimization methodology and a supporting software framework: i-om. The design goals of the proposed framework were twofold. The first comprises the decoupling, as much as possible, of the optimization process from the application specific processing tasks. In order to attain this goal, we opted to make use of intelligent techniques (i.e. metaheuristics). The second goal is to allow remote operation, and consequently great portability and interoperability, between the optimization tools and the visualization application. To fulfill the latter requirement the proposed framework was designed with the ability to communicate with external application using a specifically developed high level message protocol. The optimization framework was implemented and the paper presents illustrative results demonstrating the usefulness and effectiveness of the proposed approach..

Abstract

Abstract
With the advent of GPU programmability, many applications have transferred computational intensive tasks into it. Some of them compute intermediate data comprised by a mixture of relevant and irrelevant elements in respect to further processing tasks. Hence, the ability to discard irrelevant data and preserve the relevant portion is a desired feature, with benefits on further computational effort, memory and communication bandwidth. Parallel stream compaction is an operation that, given a discriminator, is able to output the valid elements discarding the rest. In this paper we contribute two original algorithms for parallel stream compaction on the GPU. We tested and compared our proposals with state-of-art algorithms against different data-sets. Results demonstrate that our proposals can outperform prior algorithms. Result analysis also demonstrate that there is not a best algorithm for all data distributions and that such optimal setting is difficult to be achieved without prior knowledge of the data characteristics.

Abstract
Stream Compaction is an important task to perform in the context of data parallel computing, useful for many applications in Computer Graphics as well as for general purpose computation on graphics hardware. Given a data stream containing irrelevant elements, stream compaction outputs a stream comprised by the relevant elements, discarding the rest. The compaction mechanism has the potential to enable savings on further processing, memory storage and communication bandwidth. Traditionally, stream compaction is defined as a monotonic (or stable) operation in the sense that it preserves the relative order of the data. This is not a full requirement for many applications, therefore we distinguish between monotonic and non-monotonic algorithms. The latter motivated us to introduce the Jumping Jack algorithm as a new algorithm for non-monotonic compaction. In this paper, experimental results are presented and discussed showing that, although simple, the algorithm has interesting properties that enable it to perform faster than existent state-of-the-art algorithms, in many circumstances.